Utilization of second derivative photoplethysmographic features for myocardial infarction classification

Abstract

Myocardial infarction (MI) is a common disease that causes morbidity and mortality. The current tools for diagnosing this disease are improving, but still have some limitations. This study utilised the second derivative of photoplethysmography (SDPPG) features to distinguish MI patients from healthy control subjects. The features include amplitude-derived SDPPG features (pulse height, ratio, jerk) and interval-derived SDPPG features (intervals and relative crest time (RCT)). We evaluated 32 MI patients at Pusat Perubatan Universiti Kebangsaan Malaysia and 32 control subjects (all ages 37–87 years). Statistical analysis revealed that the mean amplitude-derived SDPPG features were higher in MI patients than in control subjects. In contrast, the mean interval-derived SDPPG features were lower in MI patients than in the controls. The classifier model of binary logistic regression (Model 7), showed that the combination of SDPPG features that include the pulse height (d-wave), the intervals of “ab”, “ad”, “bc”, “bd”, and “be”, and the RCT of “ad/aa” could be used to classify MI patients with 90.6% accuracy, 93.9% sensitivity and 87.5% specificity at a cut-off value of 0.5 compared with the single features model.

Keywords:

• Photoplethysmography
• feature extraction
• myocardial infarction
• classification
• second derivative

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Acknowledgements

This research was partially supported by the Ministry of Higher Education, Malaysia (MOHE) under Fundamental Research Grant Scheme: FRGS/1/2016/TK04/UKM/02/5 and Universiti Kebangsaan Malaysia under the Research University Grant: DLP-032–2013.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This research was partially supported by the Ministry of Higher Education, Malaysia (MOHE) under Fundamental Research Grant Scheme: FRGS/1/2016/TK04/UKM/02/5 and Universiti Kebangsaan Malaysia under the Research University Grant: DLP-032–2013.